Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsNowadays, with the oversupply of several different solutions in the private Health Insurance
sector and the constantly increasing demand for value for money services from the client’s
perspective, it becomes clear that Insurance Companies shouldn’t only strive for excellence
but also engage their client base by offering solutions that are more suitable to their needs.
This project aims, using the power that predictive models can provide, to predict the existing
Health Insurance clients who are willing to move in a higher tier product. The case presented
above could be described under the term of upselling. The final model will be used for a
personalized marketing campaign in one of the most prominent bancassurances in Portugal.
At the moment the ongoing upselling campaign, uses only few eligibility criteria.
The outcome of the model has as a goal to assign a probability to each client who is eligible to
be contacted for this campaign. The data that were retrieved to train the model, had a buffer
period of one week from when the ‘event’ took place. This is crucial for the business, because
there is always the time-to-market parameter which should be taken into consideration in the
real world.
The tools that were used for completing this Data Mining project were mostly SAS Enterprise
Guide and SAS Enterprise Miner. All the Data Marts that were needed for the particular
project, were built and loaded in SAS, so there were no obstacles or connectivity issues. For
data visualization and reporting, Microsoft PowerBI was used.
Some of the tables in the Data Marts, are being updated in a daily and other in a monthly
basis. Of course, all the historical information is being stored in separate tables, so there is no
information loss or discrepancies.
Finally, the methodology that was followed for the implementation of the Data Mining project
was a hybrid framework between the SEMMA approach as it is the one that is proposed by
SAS Institute to carry out the core tasks of model development and CRISP-DM
